Authors: Sachin Sing, Neelanshu Pande, Jay Prakash Pandey, Yatharth Singh
Abstract: Skin conditions are among the most widespread health concerns globally, often triggered by factors such as fungal and bacterial infections, allergies, viruses, genetic predispositions, or exposure to chemicals. Additionally, environmental influences—such as ultraviolet (UV) radiation, pollution, and varying climate conditions—play a significant role in the development of skin disorders. Early detection and diagnosis are crucial for effective treatment. Traditionally, skin diseases have been identified through biopsies and manual assessment by dermatologists. However, advancements in laser and photonics-based medical technologies have significantly enhanced the speed and precision of skin disease diagnosis. Despite this progress, such high-end diagnostic tools remain costly and less accessible. As a cost-effective alternative, image processing techniques have emerged, enabling the creation of automated dermatological screening systems at preliminary stages. In this work, we introduce a hybrid diagnostic model that integrates deep learning (DL) and machine learning (ML) approaches. Patients submit images of affected skin areas, which serve as input to the system. The primary goal of this project is to accurately identify the specific type of skin disease and suggest appropriate treatments. Employing a range of ML and DL algorithms, the proposed method not only enhances diagnostic accuracy but also accelerates the entire process.
